Indian Sign Language Recognition

– Indian Sign Language Recognition System provides a robust solution towards real-time sign language recognition, particularly for the deaf and hard-of-hearing. The system compared to traditional approaches relying more on vision-based features and less on contextual awareness utilizes deep generative models and transfer learning techniques to provide improved accuracy. The constructed approach enunciates the challenge of temporal boundary localizations among continuous gestures as weak supervised learning, where boundaries among continuous gestures are indeterminate. As a counter measure to this, the system employs a Generative Adverserial Network (GAN) architecture for generating natural and realistic sign language gestures. The model is trained from a commercial- grade dataset of digits (0–9), alphabets (A–Z), and 50 static word signs. The system enhances the training data by generating synthetic gestures using GANs to assist in the optimization of the recognition model. The system uses Deep Convolutional GAN (DCGAN) and Super-Resolution GAN (SRGAN) for producing high-quality images. For the classification of signs, the system uses transfer learning models including ResNet-50, VGG-19, and AlexNet. The pre- trained knowledge is utilized in the models to increase the ability of the system to classify the signs. By concurrent training of the discriminator and generator, the model is able to learn how to generate and identify realistic sign gestures effectively. The proposed system achieves a high accuracy of 92.5%, which indicates its effectiveness in real-world application environments. In addition to the technical advancement in continuous sign recognition, the system fills an important communicational gap that would otherwise affect the hearing impaired. By converting sign language gestures into textual form in real-time, the Indian Sign Language Recognition System is capable of dramatically improving communication and accessibility and thus acting as an effective tool in educational, corporate, and social settings for the deaf and hard-of-hearing population.